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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Uterine_Carcinosarcoma"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Uterine_Carcinosarcoma/cohort_info.json"
# Review all cohort directories
cohorts = os.listdir(tcga_root_dir)
cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')]
# Choose uterine carcinosarcoma cohort since it directly matches our target trait
cohort_dir = "TCGA_Uterine_Carcinosarcoma_(UCS)"
cohort_path = os.path.join(tcga_root_dir, cohort_dir)
# Get clinical and genetic data file paths
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_path)
# Load the data files
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']
# Get clinical data path
print("\nAge columns preview:")
print({'age_at_initial_pathologic_diagnosis': ['54', '69', '73', '67', '86']})
print("\nGender columns preview:")
print({'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']})
# Analyze the age column candidates
age_col = 'age_at_initial_pathologic_diagnosis' # Contains valid numeric age values
# Analyze the gender column candidates
gender_col = 'gender' # Contains standard gender labels
# Print chosen columns
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# Select appropriate demographic columns
age_col = 'age_at_initial_pathologic_diagnosis' # This is more directly usable than days_to_birth
gender_col = 'gender'
# 1. Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
# 2. Normalize gene symbols in genetic data
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_genetic_df.to_csv(out_gene_data_file)
# 3. Link clinical and genetic data
linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True)
# 4. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 5. Check for bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 6. Validate and save cohort info
note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort="TCGA",
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=is_biased,
df=linked_data,
note=note
)
# 7. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)
print(f"Linked data saved to {out_data_file}")
print("Shape of final linked data:", linked_data.shape)
else:
print("Dataset was found to be unusable and was not saved") |